Embarking on an artificial intelligence journey without a rigorous data quality check is akin to setting sail without a compass. The consequences can be dire, with poor data quality estimated to cost organizations a staggering $12.9 million annually due to wasted resources and missed opportunities. This is the sobering reality highlighted by industry experts, yet a crucial shift is underway.
Organizations are increasingly recognizing the paramount importance of data quality, moving from a reactive approach to a more proactive and strategic stance. Ronnie Sheth, CEO of SENEN Group, an AI strategy, execution, and governance firm, observes this evolving landscape. With extensive experience in the data and AI domain, Sheth notes a common pitfall: companies rushing into AI adoption without adequate preparation. “Companies jump into adopting AI before they’re ready,” she states. Often, an executive mandate drives AI initiatives, but a lack of a clear blueprint or roadmap leads to impressive user engagement statistics with no tangible, measurable outcomes.
Even as recently as 2024, Sheth witnessed numerous organizations struggling with data that was far from ready for advanced AI applications. “Not even close,” she remarks. The conversation has now pivoted towards practicality and strategy. Companies are now approaching SENEN Group for foundational data work before diving into immediate AI implementation, understanding that robust data is the bedrock of any successful AI endeavor.
“When companies like that come to us, the first course of order is really fixing their data,” Sheth explains. “The next course of order is getting to their AI model. They are building a strong foundation for any AI initiative that comes after that.” By addressing data quality first, organizations can then confidently build and deploy multiple AI models and solutions, assured of accurate and reliable outputs.
SENEN Group’s expertise spans the entire data lifecycle, enabling organizations to recalibrate their strategies. Sheth points to a client who initially sought a data governance initiative. However, upon closer examination, it became clear that a comprehensive data strategy—defining the ‘why’ and ‘how’ and desired outcomes—was needed first, followed by governance and a roadmap for an operational model. This client has since progressed from raw data to descriptive and predictive analytics, and SENEN Group is now developing their AI strategy.
This emphasis on practical, value-driven initiatives is central to Sheth’s perspective on enterprise AI adoption. “Now would be the time to get practical with AI, especially enterprise AI adoption, and not think about ‘look, we’re going to innovate, we’re going to do pilots, we’re going to experiment,'” she advises. “Now is not the time to do that. Now is the time to get practical, to get AI to value. This is the year to do that in the enterprise.”
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/16938.html
